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A comparison of nonlinear filters and multi-sensor fusion for tracking boost-phase ballistic missiles

Published January 2009

Title from cover

"Prepared for: U.S. Missile Defense Agency"--Cover

"January 2009"--Cover


Author(s) subject terms: Boost-phase Missile Defense, Impulse Modeling, IMPULSE, RF sensors, Multiple Hypotheses Tracking, Extended Kalman Filtering, Unscented Kalman Filtering, Particle Filtering, Unscented Particle Filtering, Multi-sensor fusion, Extended Information Filter

Includes bibliographical references (p. 77-79)

Technical report

This report studies two aspects of tracking ballistic missiles during boost phase. The first part compares the performance of several nonlinear filtering algorithms in tracking a single target: the extended Kalman filter (EKF); the unscented Kalman filter (UKF); the particle filter (PF); and the particle filter with UFK update (UPF). Measurements are range, azimuth and elevation. In the absence of measurement error, all algorithms work well except for the PF, which does not converge. With measurement noise (standard deviations of 10 meters and 1 degree) the EFK also performs poorly, while the UPF is the top performer (although it is also the most computationally intensive). The second part compares the extended information filter (EIF) with earlier work on track scoring to perform sensor/data fusion in a multi-hypothesis framework. Here we find that the EIF handily outperformed other fusion algorithms based on track scoring that we tested

Funding number: MD7080101P0630

Publisher Monterey, California : Naval Postgraduate School
Pages 98
Language en_US
Call number ocn611582211
Digitizing sponsor Naval Postgraduate School, Dudley Knox Library
Book contributor Naval Postgraduate School, Dudley Knox Library
Collection navalpostgraduateschoollibrary; fedlink; americana

Full catalog record MARCXML

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